Literature DB >> 23969193

Signal integral for optimizing the timing of defibrillation.

Xiaobo Wu1, Joe Bisera, Wanchun Tang.   

Abstract

OBJECTIVE: The possibility of successful defibrillation decreases with an increased duration of ventricular fibrillation (VF). Futile electrical shocks are inversely correlated with myocardial contractile function and long-term survival. Previous studies have demonstrated that various ECG waveform analyses predict the success of defibrillation. This study investigated whether the absolute amplitude of pre-shock VF waveform is likely to predict the success of defibrillation.
METHODS: ECG recordings of 350 out-of-hospital cardiac arrest (OOHCA) patients were obtained from the automated external defibrillator (AED) and analyzed by the method of signal integral. Successful defibrillation was defined as organized rhythm with heart rate ≥40beat/min commencing within one min of post-shock period and persisting for a minimum of 30s.
RESULTS: Signal integral was significantly greater in successful defibrillation than unsuccessful defibrillation (81.76±32.3mV vs. 34.9±15.33mV, p<0.001). The intersection of the sensitivity and specificity curve provided a threshold value of 51mV. The corresponding values of sensitivity, specificity, positive predictive and negative predictive values for successful defibrillation were 90%, 86%, 80% and 93%, respectively. The receiver operator curve further revealed that signal integral predicted the likelihood of successful defibrillation (area under the curve=0.949).
CONCLUSIONS: Signal integral predicted successful electrical shocks on patients with ventricular fibrillation and have potential to optimize the timing of defibrillation and reduce the number of electrical shocks.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  Defibrillation; Electrocardiography; Ventricular fibrillation

Mesh:

Year:  2013        PMID: 23969193     DOI: 10.1016/j.resuscitation.2013.08.005

Source DB:  PubMed          Journal:  Resuscitation        ISSN: 0300-9572            Impact factor:   5.262


  8 in total

1.  Ventricular Fibrillation Waveform Analysis During Chest Compressions to Predict Survival From Cardiac Arrest.

Authors:  Jason Coult; Jennifer Blackwood; Lawrence Sherman; Thomas D Rea; Peter J Kudenchuk; Heemun Kwok
Journal:  Circ Arrhythm Electrophysiol       Date:  2019-01

2.  Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients.

Authors:  Mi He; Yubao Lu; Lei Zhang; Hehua Zhang; Yushun Gong; Yongqin Li
Journal:  PLoS One       Date:  2016-02-10       Impact factor: 3.240

3.  Fuzzy and Sample Entropies as Predictors of Patient Survival Using Short Ventricular Fibrillation Recordings during out of Hospital Cardiac Arrest.

Authors:  Beatriz Chicote; Unai Irusta; Elisabete Aramendi; Raúl Alcaraz; José Joaquín Rieta; Iraia Isasi; Daniel Alonso; María Del Mar Baqueriza; Karlos Ibarguren
Journal:  Entropy (Basel)       Date:  2018-08-09       Impact factor: 2.524

4.  MLWAVE: A novel algorithm to classify primary versus secondary asphyxia-associated ventricular fibrillation.

Authors:  Dieter Bender; Ryan W Morgan; Vinay M Nadkarni; Robert A Berg; Bingqing Zhang; Todd J Kilbaugh; Robert M Sutton; C Nataraj
Journal:  Resusc Plus       Date:  2020-12-14

Review 5.  [Adult advanced life support].

Authors:  Jasmeet Soar; Bernd W Böttiger; Pierre Carli; Keith Couper; Charles D Deakin; Therese Djärv; Carsten Lott; Theresa Olasveengen; Peter Paal; Tommaso Pellis; Gavin D Perkins; Claudio Sandroni; Jerry P Nolan
Journal:  Notf Rett Med       Date:  2021-06-08       Impact factor: 0.826

6.  Adult Advanced Life Support: 2020 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations.

Authors:  Jasmeet Soar; Katherine M Berg; Lars W Andersen; Bernd W Böttiger; Sofia Cacciola; Clifton W Callaway; Keith Couper; Tobias Cronberg; Sonia D'Arrigo; Charles D Deakin; Michael W Donnino; Ian R Drennan; Asger Granfeldt; Cornelia W E Hoedemaekers; Mathias J Holmberg; Cindy H Hsu; Marlijn Kamps; Szymon Musiol; Kevin J Nation; Robert W Neumar; Tonia Nicholson; Brian J O'Neil; Quentin Otto; Edison Ferreira de Paiva; Michael J A Parr; Joshua C Reynolds; Claudio Sandroni; Barnaby R Scholefield; Markus B Skrifvars; Tzong-Luen Wang; Wolfgang A Wetsch; Joyce Yeung; Peter T Morley; Laurie J Morrison; Michelle Welsford; Mary Fran Hazinski; Jerry P Nolan
Journal:  Resuscitation       Date:  2020-10-21       Impact factor: 5.262

7.  Predict Defibrillation Outcome Using Stepping Increment of Poincare Plot for Out-of-Hospital Ventricular Fibrillation Cardiac Arrest.

Authors:  Yushun Gong; Yubao Lu; Lei Zhang; Hehua Zhang; Yongqin Li
Journal:  Biomed Res Int       Date:  2015-09-02       Impact factor: 3.411

8.  Combining multiple ECG features does not improve prediction of defibrillation outcome compared to single features in a large population of out-of-hospital cardiac arrests.

Authors:  Mi He; Yushun Gong; Yongqin Li; Tommaso Mauri; Francesca Fumagalli; Marcella Bozzola; Giancarlo Cesana; Roberto Latini; Antonio Pesenti; Giuseppe Ristagno
Journal:  Crit Care       Date:  2015-12-10       Impact factor: 9.097

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.